Titlebook: Universal Time-Series Forecasting with Mixture Predictors; Daniil Ryabko Book 2020 Springer Nature Switzerland AG 2020 Time Series.Forecas

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Conclusion and Outlook,pects of the phenomenon. Yet other aspects remain completely unknown, and there is no hope that the process generating the data indeed comes from the model class. For this reason, the statistician may be content with having non-zero error no matter how much data may become available now or in the fu

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Book 2020orm of stochastic dependence. All the results presented are theoretical, but they concern the foundations of some problems in such applied areas as machine learning, information theory and data compression.

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persistence

Prediction in KL Divergence,This chapter contains the main results of this volume, it is devoted to the problems of prediction with KL loss.

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Daniil RyabkoConsiders problem of sequential probability forecasting in the most general setting.Results presented concern the foundations of problems in areas such as machine learning, information theory and data

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Ringworm

Notation and Definitions, …, ... We consider stochastic processes (probability measures) on . where . is the sigma-field generated by the (countable) set . of cylinders, . where the words .. take all possible values in .. We use . for expectation with respect to a measure ..